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- W2351612410 abstract "With neural networks, the main difficulty in improving the model generalization capability is controlling the complexity of the model. This paper investigates a Bayesian approach for neural network learning. Prior knowledge about the model parameters can be incorporated within analysis and combined with training data to control the complexity of the different parts of model. A Markov chain Monte Carlo algorithm is used to construct a Markov chain whose equilibrium distribution is the desired posterior probability density. Predictions are calculated as average over a large number of model samples. The performance and advantage of this approach are compared with conventional neural network methods in two real applications." @default.
- W2351612410 created "2016-06-24" @default.
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- W2351612410 date "2003-01-01" @default.
- W2351612410 modified "2023-09-23" @default.
- W2351612410 title "BAYESIAN NEURAL NETWORK CLASSIFIER WITH PRIOR KNOWLEDGE" @default.
- W2351612410 hasPublicationYear "2003" @default.
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